Efficacy and Synergy with Cisplatin of an Adenovirus Vectored Therapeutic E1E2E6E7 Vaccine against HPV Genome–Positive C3 Cancers in Mice
Bibliographic record
Abstract
Human papillomavirus (HPV) infections are the main cause of cervical and oropharyngeal cancers. As prophylactic vaccines have no curative effect, an efficient therapy would be highly desired. Most therapeutic vaccine candidates target only a small subset of HPV regulatory proteins, namely, E6 and E7, and are therefore restricted in the breadth of their immune response. However, research has suggested E1 and E2 as promising targets to fight HPV+ cancer. Here, we report the design of adenoviral vectors efficiently expressing HPV16 E1 and E2 in addition to transformation-deficient E6 and E7. Vaccination elicited vigorous CD4+ and CD8+ T-cell responses against all encoded HPV16 proteins in outbred mice and against E1 and E7 in C57BL/6 mice. Therapeutic vaccination of C3 tumor-bearing mice led to significantly reduced tumor growth and enhanced survival for both small and established tumors. Tumor biopsies revealed increased numbers of tumor-infiltrating CD8+ T cells in treated mice. Cisplatin enhanced the effect of therapeutic vaccination, accompanied by enhanced infiltration of dendritic cells into the tumor. CD8+ T cells were identified as effector cells in T-cell depletion assays, seemingly under regulation by FoxP3+CD4+ regulatory T cells. Finally, therapeutic vaccination with Ad-Ii-E1E2E6E7 exhibited significantly enhanced survival compared with vaccination with two peptides each harboring a known E6/E7 epitope. We hypothesize that this difference could be due to the induction of additional T-cell responses against E1. These results support the use of this novel vaccine candidate targeting an extended set of antigens (Ad-Ii-E1E2E6E7), in combination with cisplatin, as an advanced strategy to combat HPV+ cancers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".